DBTB INT Grant Ingersoll

I'm granting or saw him the CTO and co-founder of lucid works well I'm speaking on search and machine learning something I'm pretty passionate about and really looking forward to seeing how data scientists think about search because I think a lot of data scientists and data engineers don't always think of search is one of the key things in their arsenal if you will so I'm trying to help change that perception and show them interesting things they can do with search that perhaps they can't do with other data analysis tools well it's you know like anything I mean I think once you can start tapping into how people you know for me the reason why I data so interesting is because of the people on the other side of it right it's one thing to just have any arbitrary data set but then when you start to think about how people you know what are people actually doing with these systems that they're interacting with what can you learn about them how can you help them uncover things that perhaps they hadn't seen before help them solve problems that perhaps weren't solvable before so in a lot of ways it data is the ultimate tool when a democracy around how we as a citizen citizenry can can evolve to make sure we hold our leaders accountable make sure we understand what uh what's right and wrong and and things like that so that to me is the exciting part is thinking about how we all can benefit from from each other that way well I mean I think the main insight is all around how we can leverage some is really great open source tools how we can get beyond you know since I'm so focused on search a lot of people in search tend to think about keywords and 10 blue links I want them to think about there's a lot of other use cases for search at the end of the day search is really good at ranking things and saying here's what's important and so if you start to see start the leverage search in that way you can solve a lot of interesting problems that you would struggle to solve with other tools wow that's it that's a tough question I tend to focus a lot more on the data engineering side I think the main difference is that data engineer is all about putting things into production and building real systems and so I tend to focus a lot more on that and you know at the end of the day they they overlap obviously a lot but you know the key is obviously not only having good programming skills but also having good math skills and the ability to reason about the data and again I think at the end of the day there's still that connection back to the users and thinking about why are they doing those things like Ricardo and his keynote today talked about all the biases that we have in our systems thinking about you know at the end of the day the biases are all by people and that's the thing that we have to keep grounded and otherwise we just you know we're just going to be hung up on on the data and hey I got I got more data than you or I've got more machines than you and and not on the how are we solving problems for people you